Friona fell 10-8 to Boys Ranch in five innings on Monday at Friona despite racking up seven hits and eight runs. Friona was led by a flawless day at the dish by Hunter Sundre, who went 2-2 against Boys Ranch pitching. Sundre singled in the third inning and tripled in the fourth inning … Friona piled up the steals, swiping eight bags in all …

Narrative Science’s algorithms built the article using pitch-by-pitch game data that parents entered into an iPhone app called GameChanger. Last year the software produced nearly 400,000 accounts of Little League games. This year that number is expected to top 1.5 million.

For Narrative Science’s CTO and cofounder, Kristian Hammond, these stories are only the first step toward what will eventually become a news universe dominated by computer-generated stories. How dominant? “More than 90 percent,” says Hammond.

This robonews tsunami, he insists, will not wash away the remaining human reporters who still collect paychecks. Whew! Was getting worried there. — Editor.

Instead the universe of newswriting will expand dramatically, as computers mine vast troves of data to produce ultracheap, totally readable accounts of events, trends, and developments that no journalist is currently covering.

That’s not to say that computer-generated stories will remain in the margins, limited to producing more and more Little League write-ups and formulaic earnings previews. Hammond was recently asked for his reaction to a prediction that a computer would win a Pulitzer Prize within 20 years. He disagreed. It would happen, he said, in five.

Narrative Science’s writing engine requires several steps. First, it must amass high-quality data. Then the algorithms must fit that data into some broader understanding of the subject matter. (For instance, they must know that the team with the highest number of “runs” is declared the winner of a baseball game.) So Narrative Science’s engineers program a set of rules that govern each subject, be it corporate earnings or a sporting event.

But how to turn that analysis into prose? The company has hired a team of “meta-writers,” trained journalists who have built a set of templates. Then comes the structure.

Once Narrative Science had mastered the art of telling sports and finance stories, the company realized that it could produce much more than journalism. Indeed, anyone who needed to translate and explain large sets of data could benefit from its services. Requests poured in from people who were buried in spreadsheets and charts.

It turned out that those people would pay to convert all that confusing information into a couple of readable paragraphs that hit the key points.

When the company was just getting started, meta-writers had to painstakingly educate the system every time it tackled a new subject. But before long they developed a platform that made it easier for the algorithm to learn about new domains.

Narrative Science’s main rival in automated story creation, a North Carolina company founded as Stat Sheet, has broadened its mission in similar fashion.

And the subject matter keeps getting more diverse. Narrative Science was hired by a fast-food company to write a monthly report for its franchise operators that analyzes sales figures, compares them to regional peers, and suggests particular menu items to push. What’s more, the low cost of transforming data into stories makes it practical to write even for an audience of one.

For now, though, journalism remains at the company’s core. And like any cub reporter, Narrative Science has dreams of glory — to identify and break big stories. To do that, it will have to invest in sophisticated machine-learning and data-mining technologies. It will also have to get deeper into the business of understanding natural language, which would allow it to access information and events that can’t be expressed in a spreadsheet.

Hammond believes that as Narrative Science grows, its stories will go higher up the journalism food chain — from commodity news to explanatory journalism and, ultimately, detailed long-form articles. Maybe at some point, humans and algorithms will collaborate, with each partner playing to its strength.

comments 28

An algorithm to write a better news story than a human reporter is just half the job.
The other half is an algorithm to read a news story and write a better comment than a human reader.
How am I doing, folks?
Now you humans can all take a break and do something better.
Leave news writing, reading, and commenting to us robots.
Robo Reader

An algorithm write a better news story than a human reporter is just half the job.
The other half is an algorithm to read a news story and write a better comment than a human reader.
How am I doing, folks?
Robo Reader

“mastered the art?” please. The sample is completely artless, and the difference between that robo-crud and Jimmy Breslin would be clear to
sleepy high school freshman. If any art has been mastered, it is that of hyperbole.

Oh sure. Machines will never surpass humans in writing. Talk to Gary Gasparov(former chessmaster) about deep blue or the former Jeapordy! champions about Watson. How about any airline pilot about landing an airliner in foul weather? We evolve by adopting our tools…for better or worse.

Interesting, although I doubt a computer could write pulitzer prize winning material in such a short time. The analytics involved are fundamentally too complex for a machine to adequately analyze and interpret* data. This will likely change as algorithms adopt human-level cognitive abilities, but as such, the time has not yet come!

The Narrative Science (www.narrativescience.com) technology is a step forward in giving a human voice to the world of data. By its nature, it improves our interactions with data by both finding the interesting correlations, trends, inflection points and predicators in a data set and then communicating what it has found in the form of a clear, clean narrative. The implication is that for the growing world of data, this technology will help us find and report on focused insight in those data sets and communicate them to the world.

Of course the technology has the ability to communicate insights found in these data sets to the smallest of audiences, down to an “audience of one,” and do so at scale. So we can use it to write over 2 million game recaps for Little League games on an annual basis. Or use it to take high school educational opportunity data gathered by Pro Publica and generate a profile with comparisons and discussion of economic factors for every high school in the country (http://narrativescience.com/blog/quill-and-propublica-turn-numbers-into-knowledge/). Or even examine the data associated with our medical histories, fitness goals, or educational path, and transform them into clear and easy-to-understand natural language descriptions that we need to know and do.

For those worried about bias, please understand that, unlike humans, a configurable piece of software can actually be inspected for things such as bias. That is, the bias, if it exists, is both explicit and transparent, and bias in a machine is both easier to see and to manage.

As to jobs, of course the machine is moving in on more and more of our intelligence work force. And, as more and more data comes on line, systems like Narrative Science’s technology, Quill™, will be able to perform a deeper and better job of both figuring out what is happening in the world and then communicating it. There will always be aspects of our life and world that will not hit the data level, but the more that does, the more we will be able to use it in automated reasoning systems.

As to the more qualitative approaches to reporting, sure we need those. But also recall the performance difference between the political pundits in the last election cycle and Nate Silver. Silver strove to remove bias and qualitative assessment from his equations and, as a result, built a better set of predictive models. Sure, he didn’t take in the thoughts of people “going with their gut,” but that’s why he beat them.

This is a coming technology. The issue is not whether or not it is happening; it is inevitable. The question is how we use it most effectively to create a world in which the data about our world and lives currently controlled by the computer, and a very few data analysts, can be freed and transformed into natural communication that can actually help us rather than force us to understand it from the machine’s perspective.

How very interesting. It could be a way to eliminate human bias from the journalistic equation. Assuming, of course, that the algorithms are written so that they don’t slant news.

I’m, personally, not too worried about “job loss” from these machine intelligences. There will always be worthwhile work that needs to be done and people will always be paid for doing good work. Nothing is infinite within the universe so we will always have to deal with scarcity at some point. This is no different.

This will be awesome for the illuminati. If anyone whatched Foster Gambels Thrive movie, he talked about how a few rich families were controlling the media. The take back our government article also said that a few families were giving us false choices for political office. Soon there will be no independent rogue journalists to rock their boat.

Soon Katie Curric and Matt Lauer will be replaced with digitally synthesized news castors. They will always be young and won’t cost the media outlets boatloads of money! Ya gotta love the laws of economics.

Just wanted to echo Ray’s callous perspective. Don’t worry, this is just like the coming of the industrial revolution. Now you can train for a higher paying job. You didn’t want to do that menial job. Now you can be more creative..( try not to be a Ludite!)

As a writer who deals with business intelligence for learning, I can’t help but be worried for the future of those with brains designed more for qualitative functions and analysis rather than quantitative work. Though as someone interested in science per se, such developments are great from a singularity perspective.I wonder if scientists shouldn’t focus on development of technologies that help align neural performance keeping in mind economic needs. With the focus on STEM that seems to be the call to action of most developed nations, I wonder what is the future of individuals who simply do not have the higher quantitative and logical capabilities required to function effectively in such roles? Because nature cannot lead to the necessary changes in genetic patterns that lead to more individuals being born with the necessary raw skills, at the rate economics determines. Arguments stating that we need all kinds of people are valid but considering the economic realities of this world, I believe we need a system that can modify natural skill sets and talents to align with actual economically viable roles. Perhaps this sounds too much like interfering with free will and nature but unfortunately I doubt if poetry will take us to the stars. (P.S. I am an amateur poet myself)

So meta investigative journalists bring the information, meta journalist feed the algorithm, which then write the story ? It’s the writing part that’s taking over ? So no more writer block for those affected ? Trying to stay positive here ….

If by “affected” you mean “unemployed,” that’s correct. However, I’m not too worried. Hammond’s prediction that a computer would win a Pulitzer Prize within 5 years is laughable. Even 20 years is questionable.